With the recent widespread real-time adoption of IoT in several applications, there is an abundance of MVTS data. This also opens up opportunities to pre-empt failures in order to enable timely intervention, thereby, avoiding catastrophic events. As the failure events in real-world environments are rare, there is a need to detect anomalies from unlabelled data. Anomaly detection is defined as detecting data segments that deviate from the underlying distribution of the data. Detecting anomalies in a MVTS data set is challenging due to the need to model both the temporal correlations, and the correlations across the multi-variate channels[1]}, [2]}. There has been consistent research towards detecting anomaly from time-series data [3]}. With the recent developments in deep learning and their successes on image data sets, there is increased attention to develop deep learning methods for anomaly detection from MVTS [4]}, [5]}. Despite these progress, their anomaly detection accuracies are not as good as multi-channel univariate autoencoders [6]}.
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